study 2
2025-08-22
1 ReadMe
This is the analysis for study 3 of the MBC project, alias the counterfactual study.
The study had N = 50 (before exclusions), and subjects saw two conditions: Intervention and Similarity, with 15 trials per condition.
2 Set options
knitr::opts_chunk$set(echo = TRUE)
library(pacman)
pacman::p_load(
lme4,
lmerTest,
here,
patchwork,
cowplot,
viridis,
effects,
conflicted,
gt,
webshot2,
knitr,
performance,
tidyverse,
simr)
walk(c("select", "filter", "rename", "mutate", "summarise", "summarize"), ~ conflict_prefer(.x, "dplyr"))## [conflicted] Will prefer dplyr::select over any other package.
## [conflicted] Will prefer dplyr::filter over any other package.
## [conflicted] Will prefer dplyr::rename over any other package.
## [conflicted] Will prefer dplyr::mutate over any other package.
## [conflicted] Will prefer dplyr::summarise over any other package.
## [conflicted] Will prefer dplyr::summarize over any other package.## [conflicted] Will prefer simr::fixed over any other package.## R version 4.4.0 (2024-04-24)
## Platform: aarch64-apple-darwin20
## Running under: macOS 15.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/New_York
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] simr_1.0.7 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
## [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
## [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 performance_0.13.0
## [13] knitr_1.46 webshot2_0.1.1 gt_0.11.1 conflicted_1.2.0
## [17] effects_4.2-2 carData_3.0-5 viridis_0.6.5 viridisLite_0.4.2
## [21] cowplot_1.1.3 patchwork_1.2.0 here_1.0.1 lmerTest_3.1-3
## [25] lme4_1.1-35.4 Matrix_1.7-2 pacman_0.5.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 RLRsim_3.1-8 fastmap_1.1.1
## [4] promises_1.3.0 digest_0.6.35 timechange_0.3.0
## [7] lifecycle_1.0.4 survival_3.5-8 processx_3.8.4
## [10] magrittr_2.0.3 compiler_4.4.0 rlang_1.1.5
## [13] sass_0.4.9 tools_4.4.0 plotrix_3.8-4
## [16] utf8_1.2.4 yaml_2.3.8 plyr_1.8.9
## [19] xml2_1.3.6 abind_1.4-5 websocket_1.4.2
## [22] withr_3.0.0 numDeriv_2016.8-1.1 nnet_7.3-19
## [25] grid_4.4.0 fansi_1.0.6 colorspace_2.1-0
## [28] iterators_1.0.14 scales_1.3.0 MASS_7.3-60.2
## [31] insight_1.2.0 cli_3.6.4 survey_4.4-2
## [34] rmarkdown_2.29 generics_0.1.3 binom_1.1-1.1
## [37] rstudioapi_0.16.0 tzdb_0.4.0 minqa_1.2.7
## [40] DBI_1.2.2 cachem_1.0.8 chromote_0.4.0
## [43] splines_4.4.0 parallel_4.4.0 mitools_2.4
## [46] vctrs_0.6.5 boot_1.3-30 jsonlite_1.8.8
## [49] car_3.1-2 bookdown_0.39 hms_1.1.3
## [52] pbkrtest_0.5.3 jquerylib_0.1.4 glue_1.8.0
## [55] nloptr_2.1.1 ps_1.7.6 stringi_1.8.3
## [58] gtable_0.3.5 later_1.3.2 munsell_0.5.1
## [61] pillar_1.9.0 htmltools_0.5.8.1 R6_2.5.1
## [64] rprojroot_2.0.4 evaluate_0.23 lattice_0.22-6
## [67] backports_1.4.1 broom_1.0.5 memoise_2.0.1
## [70] bslib_0.7.0 Rcpp_1.0.12 gridExtra_2.3
## [73] nlme_3.1-164 mgcv_1.9-1 xfun_0.43
## [76] pkgconfig_2.0.33 Global variables
4 Study 3 - Counterfactuals
4.1 Prepare data
4.1.1 read data
data_path_cf <- paste0(base_path, "/data/data-study3-counterfactuals/pilot3")
desired_order <- c("see something", "hear something", "choose what to do",
"remember something", "think about something", "reach for something",
"sit down", "jump up and down", "kick something", "take a walk",
"get tired", "become hungry", "feel scared", "experience pain", "get sick")
file_paths <- list.files(path = data_path_cf, pattern = "^2025-", full.names = TRUE)
d <- imap_dfr(file_paths, ~ {
read_csv(.x, col_types = cols(response = col_character())) %>%
mutate(
filename = basename(.x),
subject_id = paste0("subj_", str_pad(.y, 2, pad = "0"))
)}) %>%
select(-c(mturk_participant_id, mturk_assignment_id, mturk_project_id)) %>%
relocate(subject_id, .before = "success")4.1.2 demographics
# demographics
df_demographics <- d %>%
filter(trial_id == "demographics_survey") %>%
select("trial_id", "filename",
"feedback", "age", "ethnicity", "gender", "race")
summary(df_demographics$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 20.00 29.00 35.50 38.06 45.00 70.00##
## Female Male Non-binary
## 21 28 1##
## Hispanic Non-Hispanic
## 2 48##
## American Indian/Alaska Native Asian
## 1 9
## Black/African American White
## 6 33
## White and South Asian
## 14.1.3 wrangle data
initial: 30 trials * 48 subjects = 1440 rows. dat: (30 trials / 2 trials per row) * 48 subjects = 720 rows.
TODO: rename itemB to target item.
subjects_to_exclude <- d %>%
filter(grepl("attention_check", condition)) %>%
filter(attn_correct == "no") %>%
pull(subject_id) %>%
unique()
dat <- d %>%
filter(!subject_id %in% subjects_to_exclude) %>%
filter(condition %in% c("counterfactual", "similarity")) %>%
mutate(condition = ifelse(condition == "counterfactual", "causal", condition)) %>% #meant to call it causal (both are counterfactuals)
mutate(response = as.factor(response)) %>%
mutate(condition = as.factor(condition)) %>%
select(-condition) %>%
select(subject_id, itemB, causal_itemA, similar_itemA, response_value) %>%
mutate(causal_itemA = factor(causal_itemA, levels = desired_order), #this col has unique values; we're assigning 15 lvls
similar_itemA = factor(similar_itemA, levels = desired_order), #this col has unique values; we're assigning 15 lvls
itemB = factor(itemB, levels = desired_order)) %>%
# filter(subject_id == "subj_01" & itemB == "see something") %>%
mutate(item_type = case_when(
!is.na(causal_itemA) ~ "causal_itemA",
!is.na(similar_itemA) ~ "similar_itemA"),
item = coalesce(as.character(causal_itemA), as.character(similar_itemA))) %>%
select(-causal_itemA, -similar_itemA) %>%
pivot_wider(names_from = item_type,
values_from = c(item, response_value)) %>%
rename("causal_itemA" = item_causal_itemA,
"similar_itemA" = item_similar_itemA,
"causal_itemA_counterfactual" = response_value_causal_itemA,
"similar_itemA_counterfactual" = response_value_similar_itemA) 4.2 plot dv vs conditions
4.2.1 prepare to plot
4.2.2 plot dv vs counterfactual type
figure for spp poster
main_fig <-
dat_plot %>%
mutate(counterfactual_type = factor(counterfactual_type, levels = c("similar", "causal"))) %>%
ggplot(aes(x = counterfactual_type, y = likelihood, fill = counterfactual_type)) +
stat_summary(fun.data = mean_se, geom = "bar", color = "black") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = .2) +
geom_hline(yintercept = 50, linetype = "dashed") +
labs(y = "Likelihood of Counterfactual",
x = "Preceding Item Type",
fill = "Preceding Item Type") +
scale_x_discrete(labels = c("counterfactual" = "Causal", "similarity" = "Similar")) +
theme_cowplot() +
scale_fill_manual(values = c("grey80", "#000000")) +
theme(axis.text.x = element_text(size = 25),
axis.text.y = element_text(size = 25),
axis.title.x = element_text(size = 25),
axis.title.y = element_blank(),
legend.text = element_text(size = 25),
legend.title = element_text(size = 25),
panel.background = element_rect(fill='transparent', color=NA),
plot.background = element_rect(fill='transparent', color=NA),
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
legend.background = element_rect(fill='transparent', color = NA),
legend.box.background = element_rect(fill='transparent', color = NA),
legend.position = "top",
legend.justification = "center",
legend.key.size = unit(1, "cm"),
legend.spacing.x = unit(1, "cm")
) +
ylim(0, 100)
main_fig
4.2.3 plot, facet by target items
first prune text labels
dat_plot_long <- dat_plot %>%
pivot_longer(cols = c("causal_itemA", "similar_itemA"),
names_to = "item_type",
values_to = "item") %>%
group_by(subject_id, itemB) %>%
slice(-c(2, 3))
label_data <- dat_plot_long %>%
ungroup() %>%
distinct(itemB, counterfactual_type, item)
dat_plot_long %>%
ggplot(aes(x = counterfactual_type, y = likelihood, color = counterfactual_type)) +
geom_violin() +
geom_jitter(width = .05, alpha = .1) +
stat_summary(fun = mean, geom = "point", size = 4, color = "red") +
stat_summary(fun.data = mean_se, geom = "errorbar", width = .2) +
geom_hline(yintercept = 50, linetype = "dashed") +
geom_text(data = label_data, aes(label = item), y = 95, vjust = 1, size = 3.5) +
labs(y = "Likelihood of Occurring", x = "Counterfactual type") +
scale_x_discrete(labels = c("counterfactual" = "Causal", "similarity" = "Similar")) +
theme_bw() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12),
legend.position = "none") +
facet_wrap(~itemB)
4.2.4 plot single subject
dat_plot_long %>%
filter(subject_id == "subj_01") %>%
ggplot(aes(x = counterfactual_type, y = likelihood, color = counterfactual_type)) +
geom_point() +
geom_hline(yintercept = 50, linetype = "dashed") +
geom_text(aes(label = item), y = 95, vjust = 1, size = 3.5) +
labs(y = "Likelihood of Occurring", x = "Counterfactual type") +
theme_bw() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12),
legend.position = "none") +
facet_wrap(~itemB)
5 Modelling
5.1 Effect of preceding item type
dat_model <- dat_plot %>%
mutate(target_likelihood = likelihood) %>%
mutate(preceding_item_type = factor(counterfactual_type, levels = c("similar", "causal")),
subject_id = as_factor(subject_id),
causal_itemA = as_factor(causal_itemA),
similar_itemA = as_factor(similar_itemA)) %>%
select(-c("likelihood", "counterfactual_type"))
#model
model_effect_of_preceding <- lmerTest::lmer(target_likelihood ~ preceding_item_type +
(1|subject_id), data = dat_model)
summary(model_effect_of_preceding)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: target_likelihood ~ preceding_item_type + (1 | subject_id)
## Data: dat_model
##
## REML criterion at convergence: 13703.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.45444 -0.84535 -0.05665 0.80415 2.91324
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 68.28 8.263
## Residual 765.24 27.663
## Number of obs: 1440, groups: subject_id, 48
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.885 1.577 75.852 34.81 <2e-16 ***
## preceding_item_typecausal -19.317 1.458 1391.000 -13.25 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## prcdng_tm_t -0.462
#check_model(model_effect_of_condition) #binned residual issue?
#probabilities
effects_data <- allEffects(model_effect_of_preceding)
effects_df <- as.data.frame(effects_data$preceding)
effects_df## preceding_item_type fit se lower upper
## 1 similar 54.88472 1.576511 51.79221 57.97723
## 2 causal 35.56806 1.576511 32.47555 38.66056
5.3 Power analysis with simr
5.3.1 intercept
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: target_likelihood ~ preceding_item_type + (1 | subject_id)
## Data: dat_model
##
## REML criterion at convergence: 13703.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.45444 -0.84535 -0.05665 0.80415 2.91324
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 68.28 8.263
## Residual 765.24 27.663
## Number of obs: 1440, groups: subject_id, 48
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.885 1.577 75.852 34.81 <2e-16 ***
## preceding_item_typecausal -19.317 1.458 1391.000 -13.25 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## prcdng_tm_t -0.462## (Intercept) preceding_item_typecausal
## 54.88472 -19.31667# Extend the model for power analysis
model_simulate <- extend(model_effect_of_preceding, along = "subject_id", n = 200)
# Verify the extended model
length(unique(simr::getData(model_simulate)$subject_id)) #confirm that N is now 200## [1] 200## [1] "(Intercept)" "preceding_item_typecausal"# Alternative approach: Use a different test statistic
# Sometimes the t-test doesn't work well with mixed models
# Try using the z-test instead:
curve_intercept_t <- powerCurve(model_simulate,
fixed("(Intercept)", "t"),
along = "subject_id",
breaks = c(2, 3, 4, 5, 10, 20, 30, 40, 50,
60, 70, 80, 90, 100,
110, 120, 130, 140, 150,
160, 170, 180, 190, 200),
nsim = 100,
progress = FALSE)
plot(curve_intercept_t)
## Power for predictor '(Intercept)', (95% confidence interval),
## by number of levels in subject_id:
## 2: 73.00% (63.20, 81.39) - 60 rows
## 3: 99.00% (94.55, 99.97) - 90 rows
## 4: 100.0% (96.38, 100.0) - 120 rows
## 5: 100.0% (96.38, 100.0) - 150 rows
## 10: 100.0% (96.38, 100.0) - 300 rows
## 20: 100.0% (96.38, 100.0) - 600 rows
## 30: 100.0% (96.38, 100.0) - 900 rows
## 40: 100.0% (96.38, 100.0) - 1200 rows
## 50: 100.0% (96.38, 100.0) - 1500 rows
## 60: 100.0% (96.38, 100.0) - 1800 rows
## 70: 100.0% (96.38, 100.0) - 2100 rows
## 80: 100.0% (96.38, 100.0) - 2400 rows
## 90: 100.0% (96.38, 100.0) - 2700 rows
## 100: 100.0% (96.38, 100.0) - 3000 rows
## 110: 100.0% (96.38, 100.0) - 3300 rows
## 120: 100.0% (96.38, 100.0) - 3600 rows
## 130: 100.0% (96.38, 100.0) - 3900 rows
## 140: 100.0% (96.38, 100.0) - 4200 rows
## 150: 100.0% (96.38, 100.0) - 4500 rows
## 160: 100.0% (96.38, 100.0) - 4800 rows
## 170: 100.0% (96.38, 100.0) - 5100 rows
## 180: 100.0% (96.38, 100.0) - 5400 rows
## 190: 100.0% (96.38, 100.0) - 5700 rows
## 200: 100.0% (96.38, 100.0) - 6000 rows
##
## Time elapsed: 0 h 2 m 21 s5.3.2 fixed effect
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: target_likelihood ~ preceding_item_type + (1 | subject_id)
## Data: dat_model
##
## REML criterion at convergence: 13703.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.45444 -0.84535 -0.05665 0.80415 2.91324
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject_id (Intercept) 68.28 8.263
## Residual 765.24 27.663
## Number of obs: 1440, groups: subject_id, 48
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 54.885 1.577 75.852 34.81 <2e-16 ***
## preceding_item_typecausal -19.317 1.458 1391.000 -13.25 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## prcdng_tm_t -0.462## (Intercept) preceding_item_typecausal
## 54.88472 -19.31667# Extend the model for power analysis
model_simulate <- extend(model_effect_of_preceding, along = "subject_id", n = 200)
# Verify the extended model
length(unique(simr::getData(model_simulate)$subject_id)) #confirm that N is now 200## [1] 200## [1] "(Intercept)" "preceding_item_typecausal"# Alternative approach: Use a different test statistic
# Sometimes the t-test doesn't work well with mixed models
# Try using the z-test instead:
curve_cond_effect_t <- powerCurve(model_simulate,
fixed("preceding_item_typecausal", "t"),
along = "subject_id",
breaks = c(2, 3, 4, 5, 10, 20, 30, 40, 50,
60, 70, 80, 90, 100,
110, 120, 130, 140, 150,
160, 170, 180, 190, 200),
nsim = 100)## Calculating power at 24 sample sizes along subject_id## Simulating: | |Simulating: |= |Simulating: |== |Simulating: |=== |Simulating: |==== |Simulating: |===== |Simulating: |====== |Simulating: |======= |Simulating: |======== |Simulating: |========= |Simulating: |========== |Simulating: |=========== |Simulating: |============ |Simulating: |============= |Simulating: |============== |Simulating: |=============== |Simulating: |================ |Simulating: |================= |Simulating: |================== |Simulating: |=================== |Simulating: |==================== |Simulating: |===================== |Simulating: |====================== |Simulating: |======================= |Simulating: |======================== |Simulating: |========================= |Simulating: |========================== |Simulating: |=========================== |Simulating: |============================ |Simulating: |============================= |Simulating: |============================== |Simulating: |=============================== |Simulating: |================================ |Simulating: |================================= |Simulating: |================================== |Simulating: |=================================== |Simulating: |==================================== |Simulating: |===================================== |Simulating: |====================================== |Simulating: |======================================= |Simulating: |======================================== |Simulating: |========================================= |Simulating: |========================================== |Simulating: |=========================================== |Simulating: |============================================ |Simulating: |============================================= |Simulating: |============================================== |Simulating: |=============================================== |Simulating: |================================================ |Simulating: |================================================= |Simulating: |================================================== |Simulating: |=================================================== |Simulating: |==================================================== |Simulating: |===================================================== |Simulating: |====================================================== |Simulating: |======================================================= |Simulating: |======================================================== |Simulating: |========================================================= |Simulating: |========================================================== |Simulating: |=========================================================== |Simulating: |============================================================ |Simulating: |============================================================= |Simulating: |============================================================== |Simulating: |=============================================================== |Simulating: |================================================================ |Simulating: |================================================================= |Simulating: |==================================================================|( 1/24) ( 1/24) Simulating: | |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |== |( 1/24) Simulating: |=== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |==== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |===== |## boundary (singular) fit: see help('isSingular')## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |====== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |======= |( 1/24) Simulating: |======== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |========= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |========== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |=========== |( 1/24) Simulating: |============ |( 1/24) Simulating: |============= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |============== |( 1/24) Simulating: |=============== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |================ |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |================= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |================== |( 1/24) Simulating: |=================== |( 1/24) Simulating: |==================== |## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |===================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |====================== |( 1/24) Simulating: |======================= |( 1/24) Simulating: |======================== |( 1/24) Simulating: |========================= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |========================== |( 1/24) Simulating: |=========================== |## boundary (singular) fit: see help('isSingular')
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## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |============================== |( 1/24) Simulating: |=============================== |## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |================================ |( 1/24) Simulating: |================================= |## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |=================================== |## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |==================================== |( 1/24) Simulating: |===================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |====================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |======================================= |( 1/24) Simulating: |======================================== |( 1/24) Simulating: |========================================= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |========================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |=========================================== |( 1/24) Simulating: |============================================ |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |============================================= |( 1/24) Simulating: |============================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |=============================================== |( 1/24) Simulating: |================================================ |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |================================================= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |================================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |=================================================== |( 1/24) Simulating: |==================================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |===================================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |====================================================== |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |======================================================= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |======================================================== |( 1/24) Simulating: |========================================================= |## boundary (singular) fit: see help('isSingular')## ( 1/24) Simulating: |==========================================================|( 1/24) ( 2/24) ( 2/24) Simulating: | |( 2/24) Simulating: |= |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |== |( 2/24) Simulating: |=== |( 2/24) Simulating: |==== |( 2/24) Simulating: |===== |( 2/24) Simulating: |====== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |======= |( 2/24) Simulating: |======== |( 2/24) Simulating: |========= |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |========== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |=========== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |============ |( 2/24) Simulating: |============= |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |============== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |=============== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |================ |( 2/24) Simulating: |================= |( 2/24) Simulating: |================== |( 2/24) Simulating: |=================== |( 2/24) Simulating: |==================== |## boundary (singular) fit: see help('isSingular')
## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |===================== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |====================== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |======================= |( 2/24) Simulating: |======================== |( 2/24) Simulating: |========================= |( 2/24) Simulating: |========================== |( 2/24) Simulating: |=========================== |( 2/24) Simulating: |============================ |( 2/24) Simulating: |============================= |( 2/24) Simulating: |============================== |( 2/24) Simulating: |=============================== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |================================ |( 2/24) Simulating: |================================= |( 2/24) Simulating: |================================== |( 2/24) Simulating: |=================================== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |==================================== |( 2/24) Simulating: |===================================== |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |====================================== |( 2/24) Simulating: |======================================= |( 2/24) Simulating: |======================================== |( 2/24) Simulating: |========================================= |( 2/24) Simulating: |========================================== |( 2/24) Simulating: |=========================================== |( 2/24) Simulating: |============================================ |( 2/24) Simulating: |============================================= |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |============================================== |( 2/24) Simulating: |=============================================== |( 2/24) Simulating: |================================================ |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |================================================= |( 2/24) Simulating: |================================================== |( 2/24) Simulating: |=================================================== |( 2/24) Simulating: |==================================================== |( 2/24) Simulating: |===================================================== |( 2/24) Simulating: |====================================================== |( 2/24) Simulating: |======================================================= |## boundary (singular) fit: see help('isSingular')## ( 2/24) Simulating: |======================================================== |( 2/24) Simulating: |========================================================= |( 2/24) Simulating: |==========================================================|( 2/24) ( 3/24) ( 3/24) Simulating: | |( 3/24) Simulating: |= |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |== |( 3/24) Simulating: |=== |( 3/24) Simulating: |==== |( 3/24) Simulating: |===== |( 3/24) Simulating: |====== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |======= |( 3/24) Simulating: |======== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |========= |( 3/24) Simulating: |========== |( 3/24) Simulating: |=========== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |============ |( 3/24) Simulating: |============= |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |============== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |=============== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |================ |( 3/24) Simulating: |================= |( 3/24) Simulating: |================== |( 3/24) Simulating: |=================== |( 3/24) Simulating: |==================== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |===================== |( 3/24) Simulating: |====================== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |======================= |( 3/24) Simulating: |======================== |( 3/24) Simulating: |========================= |( 3/24) Simulating: |========================== |( 3/24) Simulating: |=========================== |( 3/24) Simulating: |============================ |( 3/24) Simulating: |============================= |( 3/24) Simulating: |============================== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |=============================== |( 3/24) Simulating: |================================ |( 3/24) Simulating: |================================= |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |================================== |( 3/24) Simulating: |=================================== |( 3/24) Simulating: |==================================== |( 3/24) Simulating: |===================================== |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |====================================== |( 3/24) Simulating: |======================================= |( 3/24) Simulating: |======================================== |( 3/24) Simulating: |========================================= |( 3/24) Simulating: |========================================== |( 3/24) Simulating: |=========================================== |( 3/24) Simulating: |============================================ |( 3/24) Simulating: |============================================= |## boundary (singular) fit: see help('isSingular')## ( 3/24) Simulating: |============================================== |( 3/24) Simulating: |=============================================== |( 3/24) Simulating: |================================================ |( 3/24) Simulating: |================================================= |( 3/24) Simulating: |================================================== |( 3/24) Simulating: |=================================================== |( 3/24) Simulating: |==================================================== |( 3/24) Simulating: |===================================================== |( 3/24) Simulating: |====================================================== |( 3/24) Simulating: |======================================================= |( 3/24) Simulating: |======================================================== |( 3/24) Simulating: |========================================================= |( 3/24) Simulating: |==========================================================|( 3/24) ( 4/24) ( 4/24) Simulating: | |( 4/24) Simulating: |= |( 4/24) Simulating: |== |( 4/24) Simulating: |=== |( 4/24) Simulating: |==== |( 4/24) Simulating: |===== |( 4/24) Simulating: |====== |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |======= |( 4/24) Simulating: |======== |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |========= |( 4/24) Simulating: |========== |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |=========== |( 4/24) Simulating: |============ |( 4/24) Simulating: |============= |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |============== |( 4/24) Simulating: |=============== |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |================ |( 4/24) Simulating: |================= |( 4/24) Simulating: |================== |( 4/24) Simulating: |=================== |( 4/24) Simulating: |==================== |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |===================== |( 4/24) Simulating: |====================== |( 4/24) Simulating: |======================= |( 4/24) Simulating: |======================== |( 4/24) Simulating: |========================= |( 4/24) Simulating: |========================== |( 4/24) Simulating: |=========================== |( 4/24) Simulating: |============================ |( 4/24) Simulating: |============================= |( 4/24) Simulating: |============================== |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |=============================== |( 4/24) Simulating: |================================ |( 4/24) Simulating: |================================= |( 4/24) Simulating: |================================== |( 4/24) Simulating: |=================================== |( 4/24) Simulating: |==================================== |( 4/24) Simulating: |===================================== |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |====================================== |( 4/24) Simulating: |======================================= |( 4/24) Simulating: |======================================== |( 4/24) Simulating: |========================================= |( 4/24) Simulating: |========================================== |( 4/24) Simulating: |=========================================== |( 4/24) Simulating: |============================================ |( 4/24) Simulating: |============================================= |## boundary (singular) fit: see help('isSingular')## ( 4/24) Simulating: |============================================== |( 4/24) Simulating: |=============================================== |( 4/24) Simulating: |================================================ |( 4/24) Simulating: |================================================= |( 4/24) Simulating: |================================================== |( 4/24) Simulating: |=================================================== |( 4/24) Simulating: |==================================================== |( 4/24) Simulating: |===================================================== |( 4/24) Simulating: |====================================================== |( 4/24) Simulating: 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## calculation
## Power for predictor 'preceding_item_typecausal', (95% confidence interval),
## by number of levels in subject_id:
## 2: 74.00% (64.27, 82.26) - 60 rows
## 3: 89.00% (81.17, 94.38) - 90 rows
## 4: 96.00% (90.07, 98.90) - 120 rows
## 5: 100.0% (96.38, 100.0) - 150 rows
## 10: 100.0% (96.38, 100.0) - 300 rows
## 20: 100.0% (96.38, 100.0) - 600 rows
## 30: 100.0% (96.38, 100.0) - 900 rows
## 40: 100.0% (96.38, 100.0) - 1200 rows
## 50: 100.0% (96.38, 100.0) - 1500 rows
## 60: 100.0% (96.38, 100.0) - 1800 rows
## 70: 100.0% (96.38, 100.0) - 2100 rows
## 80: 100.0% (96.38, 100.0) - 2400 rows
## 90: 100.0% (96.38, 100.0) - 2700 rows
## 100: 100.0% (96.38, 100.0) - 3000 rows
## 110: 100.0% (96.38, 100.0) - 3300 rows
## 120: 100.0% (96.38, 100.0) - 3600 rows
## 130: 100.0% (96.38, 100.0) - 3900 rows
## 140: 100.0% (96.38, 100.0) - 4200 rows
## 150: 100.0% (96.38, 100.0) - 4500 rows
## 160: 100.0% (96.38, 100.0) - 4800 rows
## 170: 100.0% (96.38, 100.0) - 5100 rows
## 180: 100.0% (96.38, 100.0) - 5400 rows
## 190: 100.0% (96.38, 100.0) - 5700 rows
## 200: 100.0% (96.38, 100.0) - 6000 rows
##
## Time elapsed: 0 h 2 m 23 s5.4 plot dv vs distances
5.4.1 prepare append distances
## Rows: 210 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): itemA, itemB, domain_itemA, domain_itemB
## dbl (3): mean_causal_distance, mean_freesort_distance, freesort_minus_causal
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.dat_distances <- dat_plot_long %>%
ungroup() %>%
rename("itemA" = item) %>%
left_join(distances %>%
select(itemB, itemA, mean_causal_distance, mean_freesort_distance, freesort_minus_causal),
by = c("itemB", "itemA")) %>%
mutate(mean_causal_distance = round(mean_causal_distance, 2),
mean_freesort_distance = round(mean_freesort_distance, 2),
freesort_minus_causal = round(freesort_minus_causal, 2)) %>%
mutate(itemB = factor(itemB, levels = desired_order))5.4.2 plot difference scores
15 similar item - target item pairs, 15 causal item - target item pairs. The more causal (right of x -axis), the less likely the counterfactual event will occur
#make separate data frame with only distances and difference scores?
#or, get the mean responses and an error bar for it?
#parcel out labels
label_distances <- dat_distances %>%
distinct(itemB, counterfactual_type, itemA, freesort_minus_causal)
#plot
dat_distances %>%
ggplot(aes(x = freesort_minus_causal, y = likelihood, color = counterfactual_type)) +
geom_point(alpha = .2) +
geom_text(data = label_distances, aes(label = itemB), y = 95, vjust = .5, size = 3.5, angle = 80) +
geom_text(data = label_distances, aes(label = itemA), y = 85, vjust = .5, size = 3.5, angle = 80) +
stat_summary(fun.data = mean_se, geom = "errorbar", width = .01, color = "black") +
stat_summary(fun = "mean", geom = "point", shape = 1, size = 3, color = "black") +
geom_smooth(method = "lm") +
geom_hline(yintercept = 50, linetype = "dashed") +
theme_bw() +
labs(y = "Likelihood of Occurring", x = "Freesort minus Causal Distance") +
theme_bw() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12))## `geom_smooth()` using formula = 'y ~ x'
5.4.3 itemwise difference scores
dat_distances %>%
ggplot(aes(x = freesort_minus_causal, y = likelihood, color = counterfactual_type)) +
geom_point(alpha = .2) +
geom_text(data = label_distances, aes(label = itemA), y = 90, size = 3.5) +
stat_summary(fun.data = mean_se, geom = "errorbar", width = .03, color = "black") +
stat_summary(fun = "mean", geom = "point", shape = 1, size = 2, color = "black") +
geom_smooth(method = "lm") +
geom_hline(yintercept = 50, linetype = "dashed") +
theme_bw() +
labs(y = "Likelihood of Occurring", x = "Freesort minus Causal Distance") +
theme_bw() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)) +
facet_wrap(~itemB)## `geom_smooth()` using formula = 'y ~ x'
5.4.4 plot individual distances
dat_distances %>%
pivot_longer(cols = c("mean_causal_distance", "mean_freesort_distance"),
names_to = "distance_type",
values_to = "distances") %>%
ggplot(aes(x = distances, y = likelihood, color = distance_type)) +
geom_jitter(width = .01, alpha = .2) +
geom_smooth(method = "lm") +
geom_hline(yintercept = 50, linetype = "dashed") +
theme_bw() +
labs(y = "Likelihood of Occurring", x = "Distance") +
theme_bw() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12)) +
facet_wrap(~item_type)## `geom_smooth()` using formula = 'y ~ x'